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20202023
most citedEntropy-Constrained Maximizing Mutual Information Quantization

6 citations · 13 across the 5 of their papers we have counts for

collaborators

6 papers

cs.LG2023

Hard-Negative Sampling for Contrastive Learning: Optimal Representation Geometry and Neural- vs Dimensional-Collapse

Ruijie Jiang, Thuan Nguyen, Shuchin Aeron +1

For a widely-studied data model and general loss and sample-hardening functions we prove that the losses of Supervised Contrastive Learning (SCL), Hard-SCL (HSCL), and Unsupervised…

cs.LG2022

Trade-off between reconstruction loss and feature alignment for domain generalization

Thuan Nguyen, Boyang Lyu, Prakash Ishwar +2

Domain generalization (DG) is a branch of transfer learning that aims to train the learning models on several seen domains and subsequently apply these pre-trained models to other…

cs.CV2021

VinaFood21: A Novel Dataset for Evaluating Vietnamese Food Recognition

Thuan Trong Nguyen, Thuan Q. Nguyen, Dung Vo +5

Vietnam is such an attractive tourist destination with its stunning and pristine landscapes and its top-rated unique food and drink. Among thousands of Vietnamese dishes, foreigner…

eess.SP20204 cited

Single-bit Quantization Capacity of Binary-input Continuous-output Channels

Thuan Nguyen, Thinh Nguyen

We consider a channel with discrete binary input X that is corrupted by a given continuous noise to produce a continuous-valued output Y. A quantizer is then used to quantize the c…

eess.SP20203 cited

On the Uniqueness of Binary Quantizers for Maximizing Mutual Information

Thuan Nguyen, Thinh Nguyen

We consider a channel with a binary input X being corrupted by a continuous-valued noise that results in a continuous-valued output Y. An optimal binary quantizer is used to quanti…

cs.IT20206 cited

Entropy-Constrained Maximizing Mutual Information Quantization

Thuan Nguyen, Thinh Nguyen

In this paper, we investigate the quantization of the output of a binary input discrete memoryless channel that maximizing the mutual information between the input and the quantize…